Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps
| Autor: | Grau, Isel, Nápoles, Gonzalo, Hoitsma, Fabian, Koutsoviti Koumeri, Lisa, Vanhoof, Koen |
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| Přispěvatelé: | Information Systems IE&IS, EAISI Health, EAISI Foundational |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | arXiv, 2023:2305.09399v2, 1-20. Cornell University Library |
| ISSN: | 2331-8422 |
| DOI: | 10.48550/arxiv.2305.09399 |
| Popis: | In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded. Comment: Accepted at the Intelligent Systems Conference (IntelliSys) 2023 and will be presented on 7-8 September 2023 |
| Databáze: | OpenAIRE |
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